Category: Papers

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node’s concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.


Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
Tomokatsu Onaga, James P. Gleeson, and Naoki Masuda
Phys. Rev. Lett. 119, 108301


Electron-Eating Microbes Found in Odd Places

The electricity-eating microbes that the researchers were hunting for belong to a larger class of organisms that scientists are only beginning to understand. They inhabit largely uncharted worlds: the bubbling cauldrons of deep sea vents; mineral-rich veins deep beneath the planet’s surface; ocean sediments just a few inches below the deep seafloor. The microbes represent a segment of life that has been largely ignored, in part because their strange habitats make them incredibly difficult to grow in the lab.


Generative Models for Network Neuroscience: Prospects and Promise

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modeling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, utility in intuiting mechanisms, and a short history on their use in network science broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including \emph{C. elegans}, \emph{Drosophila}, mouse, rat, cat, macaque, and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modeling approach for network neuroscience.


Generative Models for Network Neuroscience: Prospects and Promise
Richard F. Betzel, Danielle S. Bassett